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- He, Chunmei, et al.
(författare)
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A new vegetation index combination for leaf carotenoid-to-chlorophyll ratio : minimizing the effect of their correlation
- 2023
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Ingår i: International Journal of Digital Earth. - : Informa UK Limited. - 1753-8947 .- 1753-8955. ; 16:1, s. 272-288
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Tidskriftsartikel (refereegranskat)abstract
- The ratio of leaf carotenoid to chlorophyll (Car/Chl) is an indicator of vegetation photosynthesis, development and responses to stress. However, the correlation between Car and Chl, and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio. This study aims to investigate combinations of vegetation indices (VIs) to minimize the influence of Car-Chl correlation, thus being more sensitive to the variability in the ratio across vegetation species and sites. VIs sensitive to Car and Chl variability were combined into four candidates of combinations, using a simulated dataset from the PROSPECT model. The VI combinations were then tested using six simulated datasets with different Car-Chl correlations, and evaluated against four independent datasets. The ratio of the carotenoid triangle ratio index (CTRI) with the red-edge chlorophyll index (CIred-edge) was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability. Compared with published VIs and two machine learning algorithms, CTRI/CIred-edge also showed the optimal performance in the four field datasets. This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio, applicable for assessing vegetation physiology, phenology, and response to environmental stress. Trial registration:Clinical Trials Registry India identifier: CTRI/.
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| 2. |
- He, Chunmei, et al.
(författare)
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PROSPECT-GPR : Exploring spectral associations among vegetation traits in wavelength selection for leaf mass per area and water contents
- 2023
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Ingår i: Science of Remote Sensing. - 2666-0172. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Leaf mass per area (LMA) and equivalent water thickness (EWT) are key indicators providing information on plant growth status and agricultural management, and their retrieval is commonly done through radiative transfer models (RTMs) such as the PROSPECT model. However, the PROSPECT model is frequently hampered by the ill-posed problem as a consequence of measurement and model uncertainties. Here, we propose a wavelength selection method to improve the inversion of EWT and LMA by integrating PROSPECT with a machine learning algorithm (Gaussian process regression (GPR); PROSPECT-GPR for short). The GPR model conducted sorting of wavelengths and the PROSPECT-D was used to determine the optimal number of characteristic wavelengths. The results demonstrated that the estimation of EWT (R2 = 0.80; RMSE = 0.0021) and LMA (R2 = 0.71; RMSE = 0.0021) using the proposed wavelengths and PROSPECT inversion all exhibited superior accuracy in comparison with those from previous studies. The efficacy of PROSPECT-GPR in exploring the spectral linkage among vegetation traits was demonstrated by selecting wavelengths associated with leaf structure parameter N and EWT (1368 nm) that turn out to contribute to the estimation of LMA. The findings lay a strong foundation for understanding the spectral linkage among vegetation traits, and the proposed wavelength selection method provides valuable insights for selecting informative spectral wavelengths for RTMs inversion and designing future remote sensors.
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